Stereoscopic image display method and apparatus, method for generating 3D image data from a 2D image data input and an apparatus for generating 3D image data from a 2D image data input

ABSTRACT

2D image data are converted into 3D image data. The image is divided, on the basis of focusing characteristics, into two or more regions, it is determined to which region an edge separating two regions belongs. The regions are depth ordered in accordance with the rule that the rule that a region comprising an edge is closer to the viewer than an adjacent region and to the regions 3-D depth information is assigned in accordance with the established depth order of the regions. Preferably to each of the regions a depth is assigned in dependence on an average or median focusing characteristic of the region.

The invention relates to a stereoscopic image display method wherein 2Dimage data are converted into 3D image data and wherein focusinformation is extracted from the 2D image data and used for generatingthe 3D image data.

The invention also relates to a stereoscopic image display devicecomprising an input for 2D image data and a converter to convert theinput 2D image data into 3D image data the converter comprising a focusinformation extractor for extracting focus information from the 2D imagedata.

The invention also relates to an image display data conversion methodwherein 2D image data are converted into 3D image data and wherein focusinformation is extracted from the 2D image data and used for generatingthe 3D image data.

The invention further relates to a 3D image signal.

The invention further relates to a computer program product to be loadedby a computer arrangement, comprising instructions to generate 3D imagedata on basis of a 2D image data input, the computer arrangementcomprising processing means.

A stereoscopic image display method and device of the kind described inthe opening paragraph is disclosed in EP 1 021 049. In EP 1 021 049 a 3dimensional video image is generated from a 2 dimensional video input.The known device and method uses a foreground/background discriminatingcircuit which discriminates on the basis of focus information which isextracted from the 2 dimensional video input. A parallax control signalis outputted on the basis of edge detecting wherein sharp edges areplaced in the foreground of the 3D image.

Although the known method and device provide for a relatively simpledevice and method, it has been found that the rendered 3D imagesoccasionally are confusing images wherein depth of vision, i.e. the 3Deffect, is difficult to distinguish.

It is an object of the invention to improve 3D image rendering based ona 2D image input.

To this end the method in accordance with the invention is characterizedin that on basis of focus characteristics the image is divided into twoor more regions, it is determined to which region of the image an edgeseparating two regions belongs and a depth order is established betweenthe regions following the rule that a region comprising an edge iscloser to the viewer than an adjacent region and to the regions 3-Ddepth information is assigned in accordance with the established depthorder of the regions.

In the prior art method of EP 1 021 049 edge detection is alsoperformed. Sharp edges are placed in the foreground. This scheme,however, sometimes provides for confusing results since parts of theimages that are in reality in the foreground are given backgroundparallax and vice versa in case the background happened to be in focusand the foreground out-of-focus. This provides for confusing imageswherein the parallax information provides the viewer the cue thatcertain parts of the 3D image are on the foreground and others parts inthe background, but the actual content of the image provides the viewerwith a completely opposite cue, i.e. what is foreground according theparallax cue is background according to the actual content.

The 3D sensation is then confusing at best and often lost, especiallysince the depth cue given by the known method is usually limited. It isassumed that the human brain is capable of reconstructing a stereoscopicsense from even an imperfect depth cue. The depth cues in the prior artmethod and device are, however, sometimes at odds with each other, andmay even change from scene to scene, i.e. in one scenes the depth cuesmay be correct, followed by a sudden shift to conflicting depth cueswherein a foreground figure hides behind a background tree. The depthsensation is then lost or at least a very annoying conflict betweendepth cues is perceived by the viewer.

The method in accordance with the invention solves or at least reducesthe problem. The image is divided in regions on the basis of focusinformation, for instance the blur radius. The pixels or blocks of theimage are clustered into a number of regions having similar focuscharacteristic. Based on the focus information, e.g. the average blurper block, the image is divided into two or more regions wherein eachregion has averaged focusing characteristics. It is determined to whichregion an edge separating two regions belongs. This may e.g. be done bycomparing the sharpness (blur) of a detected edge to the average blur ofthe regions bordering either side of the edge. A blurred edge belongs toa bordering region having a high blur, whereas a sharp edge to a regionhaving a low blur. A depth ordering is performed on the regions, whereinthe rule is followed that a region comprising an edge is closer to theviewer than the adjacent region. 3D information is assigned to theregions in accordance with the depth ordering. The various regions ofthe image thus form depth layers. Dividing the image into regions isperformed by means of clustering pixels or blocks into regions. Althoughthis clustering could be done on a pixel per pixel basis, it is foundthat more robust results are obtained when, prior to division of theimage into regions, a focusing characteristic is determined per block ofpixels and the blocks are clustered into regions. Blocks are small partsof the image having n×m pixels, usually m×m, where n and m are typically2, 4, 8 or 16.

The advantage of the method in comparison to the known method is clearfor e.g. an image in which a person is seated partially behind a flowerarrangement. The person is in focus; the flower arrangement is not.Using the known method the person being in focus and thus having sharpimage edges, is given a parallax so that it seems in the foreground andimage portion depicting the flower arrangement, having a blurred edge,is given a parallax corresponding with background. This conflicts withthe true situation since the person is partially behind the flowerarrangement and not the other way around. The known method and devicethus confronts the viewer with two conflicting, in fact irreconcilable,depth cues. The parallax depth cue, putting the person on the foregroundin front of the flower arrangement, contradicts the image informationdepth cue, which shows the person seated behind the flower arrangement.

The method in accordance with the invention does not provide forconflicting depth cues. The image is divided into regions and comprisesat least two regions, for instance an in focus region comprising theperson and an out-of-focus region comprising the flower arrangement. Theedges separating the regions comprising the flower arrangement and theregion comprising the person are formed by the blurred edges of theflower arrangement. Thus the region of the image comprising the flowerarrangement is placed on the foreground, in accordance with the rulethat a region comprising the edge separating two regions is closer to aviewer than the other region. Out-of-focus foreground regions, which arebounded by blurred edges, are placed in front of in-focus backgroundregions. Thus, if there are two regions, an out-of-focus foregroundflower arrangement in front of an in-focus person, the correct parallaxis assigned to both regions. If there are three regions, an out-of-focusforeground flower arrangement, an in-focus person and an out-of-focusbackground, the correct 3D information is provided for the threeregions. It is emphasized that the results of the method in accordancewith the invention provide, in this example, results that are againstthe very core of the teaching of EP 0 121 049, which dictates that depthordering is done by placing sharp edges on the foreground.

Preferably the 3D depth information is assigned in dependence on thefocusing characteristics of the regions. The average focusingcharacteristics provides a clue as to the difference in depth betweenthe regions. This can be used to improve the 3D effect.

In preferred embodiments the number of regions is three or two.Clustering the pixels or blocks of the image into two or three regionshas proven to give good results, while requiring only limitedcalculating power. Almost all images have an in-focus part, and anout-of-focus part, the out-of-focus part sometimes being foreground,sometimes being background, so that two regions often suffice.Occasionally the out-of-focus part comprises a fore-ground andbackground part, for instance a foreground tree and a background forestand an intermediate in-focus region, in which case three regions usuallysuffice.

In a preferred embodiment a statistical distribution is made of focusingcharacteristics of pixels or blocks of the image and the number ofregions is determined in dependence on the statistical distribution.

It is found that the focusing characteristics, such a blur radius, oftencluster around a limited number of peaks, one corresponding to a smallblur radius, i.e. in focus or nearly in focus, and another or others atlarger blur radii, corresponding to out of focus parts of the image.Using these statistical data allows for a quick determination of thenumber of regions in which the region can be divided.

The image display device in accordance with the invention comprisesmeans for performing the method steps in accordance with the invention.

The invention is also embodied in a transmitter comprising means forperforming the method steps in accordance with the invention.

These and other objects of the invention will be apparent from andelucidated with reference to the embodiments described hereinafter.

In the drawings:

FIG. 1 illustrates the thin lens model;

FIGS. 2A-2C illustrate a possible method for determining blur radius;

FIG. 3 illustrates the relation between blur radius and focal plane;

FIG. 4 illustrates a statistical distribution of blur radii;

FIGS. 5A and 5B illustrate a method of determining regions;

FIG. 6 illustrates a method for deciding to which regions an edgebelongs;

FIG. 7 illustrates a method in accordance with the invention;

FIG. 8 illustrates a display device in accordance with the invention;and

FIG. 9 illustrates a transmitter in accordance with invention.

The figures are not drawn to scale. Generally, identical components aredenoted by the same reference numerals in the figures.

In a simple optical system, like a convex thin lens, objects at aparticular distance from the lens are clearly depicted (objects are infocus) on the image plane, while objects at other distances are mappedblurred (objects are defocused) proportional to their distance from theplane of focus. The latter situation for a point source is depicted inFIG. 1.

The blur behavior is according to the thin lens formula:

$\begin{matrix}{\frac{1}{f} = {\frac{1}{u} + \frac{1}{v}}} & (1)\end{matrix}$in which f represents the focal length of the lens, u is the objectdistance and v is the image distance. From the geometric relations inFIG. 1 and the lens formula, the formula for the distance u can bederived:

$\begin{matrix}{u = {{\frac{fs}{s - f - {k\;\sigma\; f}}\mspace{14mu}{if}\mspace{14mu} u} > u_{0}}} & (2) \\{u = {{\frac{fs}{s - f - {k\;\sigma\; f}}\mspace{14mu}{if}\mspace{14mu} u} < u_{0}}} & (3)\end{matrix}$wherein u₀ denotes the distance for which points are in focus. Theparameter s is the image plane to lens distance and the parameter k is aconstant determined by the characteristics of the lens system. Theparameters f, s and k are camera parameters, which can be determinedfrom camera calibration. Thus estimating the distance u of an objectinvolves determining the camera parameters and estimating the blurradius a. Thus there is a relation between the blurriness of an image,i.e. a focus characteristic and the distance.

For 2D-to-3D conversion, disparity (inverse depth) is a more relevantquantity than depth itself, as for instance the parallax for renderedviews is linear in disparity. Using the above expression it is possibleto find a relation between disparity differences between points in focusand out of focus and the blur radius a.

$\begin{matrix}{{{\frac{1}{u} - \frac{1}{u_{0}}}} = \frac{k\;\sigma}{s}} & (4)\end{matrix}$

In other words, the disparity difference to the focal plane isproportional to the blur radius. Moreover, as the amount of disparityfor rendered views can usually be changed to accommodate for thepreference of the user and/or the possibilities of the display, accuratedetermination of the camera-related constant k/s is not necessary, allthat is needed is determination of the blur radius σ, i.e. of a focuscharacteristic. In the following description, the blur radius is takenfor the focus characteristic for the simple reason that there is asimple relation between distance and blur radius. However, althoughdetermining the blur radius as the focus characteristic is preferred,due to the simple relation between blur radius and distance, othermeasures of blurriness could also be determined within the concept ofthe invention.

FIGS. 2A-C schematically illustrate a possible method for determiningblur radius σ. In FIG. 2A a blurred edge with a blur radius σ isschematically shown. The horizontal axis denotes position, the verticalaxis luminance. In FIG. 2B a filtering function is shown which is thesecond derivative of a Gaussian filter with width s. Convolution of FIG.2A and FIG. 2B provides for a function having two peaks. The distanced_(h) between the peaks can be measured reasonably adequate and therelation between the blur radius σ, filter width s and peak distanced_(h) is as follows:σ²=(d _(h)/2)² −s ²  (5)

This exemplary algorithm is robust and the results obtained for varioustypes of content were good. Taking various filter widths s for eachpixel for each filter width a value for the blur radius σ is found.Taking an average or median value of σ per pixel and then determining anaverage or median value for σ over a block wherein more pronouncededges, which have a larger height in part FIG. 2C, are given a largerweight proved to give robust results. A reasonably good distinction indetermined values for σ between the in-focus and out-focus regions isfound.

The relation between u and the blur radius σ is schematically shown inFIG. 3 and follows from equation (4).

If the parameters k and s are known from calibration, then a trueestimate of the absolute distance to the focal plane can be made, oncethe blur radius σ is known. Since this does not reveal if a blurredobject is in front of the focal plane or behind it, also at least twoimages for different focal distances need to be known for true depthestimation from the blur radius σ. However, neither of theserequirements is usually known or obtainable for arbitrary externallygiven image data such as e.g. video. A good distinction can neverthelessbe made between out-of-focus regions of the image and in focus regionsof the image and, if there are more regions, between the variousregions.

Since the formula between the disparity difference and the blur radiusgives a relation between the absolute value of the disparity differenceand the blur radius, the equation has two separate solutions. Hencedetermination of two different values of the blur radius σ does notenable depth ordering, as the same values of σ may result from an objectcloser to or further away. In FIG. 4 this is schematically shown for twodifferent values for the blur radius σ (σ1 and σ2). In principle thereare four different possible combinations of image planes possible.

FIG. 4 shows a typical distribution of blur radii for blocks within animage wherein the horizontal axis denotes the percentage of blocks witha certain blur radius. Clearly two modes centered on peaks with valuesof σ₁ and σ₂ can be distinguished corresponding in this example within-focus and out-of-focus parts of the image. Such a distribution alone,however, does not enable to provide an accurate depth ordering for tworeasons. First of all, as explained in relation to FIG. 3, there isambiguity as to the actual relative position of image planescorresponding with the peaks in FIG. 3 since more than one solution ispossible. Secondly the peaks in the distribution in σ are quite broad.This indicates that the actual blur values have a high numericaluncertainty and may not be suitable for deriving depth orderinginformation, as blur radius difference (the spread in the peaks in FIG.3) in each mode (e.g. the out-of-focus region) may exceed blur radiusdifferences between modes. Hence only using actual numerical values ofthe blur radius to decide on depth ordering and depth ordering of eachblock introduces a large amount of noise.

To nevertheless obtain reliable depth ordering the method and device inaccordance with the invention executes two steps.

In a first step the pixels or blocks of the image are clustered based ontheir focusing characteristic, thereby forming regions within the image.Within the broadest scope of the invention, also pixels could beclustered. However, the spread in values of σ for pixels is even largerthan for blocks. More robust results are obtained when, prior toclustering a focusing characteristic, in the examples given an averageor median value for the blur radius σ, is assigned on a block basis andthe blocks are clustered into regions on the basis of the block valuesfor σ. To each region an average or medium blur radius is assigned.Clustering may be done in various manners.

A simple iterative clustering algorithm may be used which always dividesthe image into two or more clusters starting from a heuristic initialclustering. The decision whether we have one, two or more clusters isthen based on the similarity of the characteristics of the clusters.

FIGS. 5A and 5B illustrate such a method wherein it is assumed thatthere are two large regions, one in focus and more or less in themiddle, surrounded by an out-of-focus region. The initial clusteringconsists of assigning the blocks on the left, top and right border (say¼ of the image) to the ‘background’ cluster C₂, and the other pixels tothe ‘foreground’ cluster C₁ (see FIG. 5A). This choice originates fromthe selection of blocks for background motion model estimation.Heuristically, one expects that the object of interest (usually theforeground) is somewhere in the center of the image, and the borders ofthe image do not contain objects of interest. For background motionmodel estimation, it is assumed that the object of interest in thecenter is the foreground. It is, however, not necessary to make such anassumption in the clustering stage. It has been observed, however, thatmost of the time the center cluster is in focus.

As the initial clustering is rather coarse and based on heuristics, arobust method to arrive at initial estimates of the blur radii for eachcluster is as follows.

A number of feature points (in our case 28), regularly distributedinside the clusters is selected. The initial blur radius value σ₁respectively σ₂ of a cluster is the median of the blur radii σ of allthose feature points.

Then an iterative procedure is carried out to refine this cluster:

Step 1: Reassign the blocks. A sweep is made over the image, and eachblock B on a cluster boundary is assigned to the cluster to which it hasthe smallest deviation to its mean focus estimate:B→C ₁ if |σ_(B)−σ₁|<|σ_(B)−σ₂|  (6)

B→C₂ else

Step 2: Update the values for σ₁ and σ₂. Blocks have been reassigned toclusters C₁ and C₂ so new average or median cluster blur radii σ₁ and σ₂are computed for each of the two (or more if there are more) clusters.

Step 3: Iterate. A new sweep is made, see step 1.

This process converges after a few (typically 4) iterations.

FIG. 5B shows the result of such iteration: two regions are formed, aforeground region C₁ with a median blur radius σ₁, and background regionC₂ with a median blur radius σ₂.

Typically this method provides for two regions, an out-of-focus regionand in a in-focus regions. These regions need not be connected, e.g. thein focus regions may comprise two separate sub regions, as may theout-of-focus region. When the statistics shows evidence of threeregions, i.e. three peaks in the σdistribution, it is possible to startwith three regions. An initial clustering may also be found bydetermining the peaks in the σ diagram, and simply assigning each blockto the peak with the best matching σ.

Once the image is divided into regions C₁, C₂, C_(3 etc), it is possibleto assign a region blur radius σ_(i) to each of the regions. The nextstep in the method and device in accordance with the invention is thatthe mutual position, i.e. which region is in front of which region, ofthe regions is determined. A decision on depth ordering has to be made.In order to do so use is made of the principle that an edge belongs tothe foremost object. FIG. 6 illustrates schematically a method fordistinguishing from this principle which edge belongs to which regions.FIG. 6 shows along the horizontal axis a position parameter, such as thex, or y coordinate, or a coordinate perpendicular to a transitionbetween two regions. Vertically the blur radius is shown. In FIG. 6schematically the transition between an in-focus region with a low valuefor blur radius σ and an out-of-focus region with a high value for σ isshown. The width W illustrates schematically the blurriness of the edge.An out-of-focus edge will have a larger width W than an in-focus edge.Schematically this is shown in the top part of FIG. 6, having a small Wand thus a sharp transition, and the bottom part, showing a large widthW and thus a blurred transition. Thus in the top part the edgeseparating the regions C₁ and C₂ belongs to the region C₁ with low blurradius σ₁. Thus region C₁ is foreground, which is indicated in thefigure by C₁(F). Region C₂ is background indicated by C₂(B). In thebottom part the width W is large. The edge separating the regions C₁ andC₂ belongs to the region C₂ with high blur radius 2. Thus “blurred”region C₂ is foreground, which is indicated in FIG. 6 by C₂(F). RegionC₁ is background indicated by C₁(B). By taking various measurementpoints along lines perpendicular to the transition lines between theregions, and taking an average or deciding for each measure point towhich the region the edge seems to belong and then voting between thedifferent measurements, it is easily found whether the edge belongs tothe an in-focus region, in which case the in-focus region lies in frontof the out-of-focus region, or to an in-focus region, in which case thein-focus region lies in front of the out-of-focus region. To put itdifferently, the width W is only dependent on the σ of one the regions,not or at least hardly on the σ of the other region. This characteristiccan be used to determine to which regions an edge separating two regionsbelong.

This is one example of a method for establishing to which region an edgebelongs.

A different method is for instance to segment the image, i.e. the findluminance or color edges in the image near the transitions between theregions and compare them to the edges between the regions as followsfrom the preceding clustering step.

Using luminance segmentation, different methods may be used to findwhich edge belongs to which regions. One way is to look at theorientation of luminance edges in the various regions near thetransition between the regions. The luminance edge corresponding to thetransition between regions is determined solely by the foreground imageand the edge or edges belonging to the foreground image often follow thetransition, i.e. they are parallel to the transition. Luminance edges inthe background tend not to have a relation to the transition.

Yet another method is the following: the image is segmented based onfocus, as explained above, and luminance edges are found near thetransitions between the regions. By determining the edge between regionsin two different ways, by luminance segmentation and by clustering onthe basis of blur radius it may be established to which region an edgebelongs. Ideally the two determinations would completely coincide, butthis is not the case. It has been found that clustering of blocks tendson average to extend the region to which an edge belongs to slightlybeyond the luminance edge because the whole edge or at least a majorpart of the edge is assigned the blur radius of the edge which belongsto the foreground object. There is thus a slight bias in clusteringwhich extends a clustered region to include the edge belonging to saidcluster. This bias does not occur for determination of edges when solelydifferences in luminance are concerned because in luminance segmentationthe transition between the regions is drawn in the middle of the edgeseparating the regions. There is thus a small difference in thedetermined position of the edge, since the clustering method based onblur radius determination as described above tends to overextend theborder of the clustered foreground region to include into a region theedge belonging to said region, whereas such tendency to overextend doesnot exist for edges solely determined on the basis of luminancesegmentation. To put it differently: luminance segmentation puts theedge exactly in the middle of the luminance transition, whereasclustering segmentation overestimates the size of the foreground region.This effect is also called morphological dilatation, i.e. the clusteringslightly dilates, i.e. increases in size, the form of the foregroundobject. This bias of the clustering method draws foreground object edgesinto the foreground cluster. This seemingly negative effect can bebrought to good use by comparing the edge as determined by luminancesegmentation to the same edge as determined by blur radius segmentation.This allows to establish to which regions an edge belongs. Blur radiusdetermination or more in particular determination of focuscharacteristics may be done using alternative algorithms. Alternativealgorithms for clustering may also be used. Depending on the usedalgorithms the so determined foreground region will overextend orunderextend in respect of edge determined by luminance edges. In bothcases it is possible to determine to which region an edge belongs bycomparing the regions determined by luminance segmentation to theregions determined by determination and clustering of focusingcharacteristics.

Depth ordering can be done simply on the basis of what region isforeground and what region is background, i.e. a fixed difference inparallax can be used to distinguishing the foreground and backgroundregions, or foremost, intermediate range and background regions,independent of the actual values σ_(i).

Preferably the blur radius estimates for the regions are converted intoa depth or inverse depth value. Given the depth orderings and σ valueswe may take the disparity of blurred objects as the disparity of infocus objects, i.e. the region with lowest σ, plus a constant time thedifference in blur radius between foreground and background.

$\frac{1}{u} \approx {\frac{1}{u_{0}} + {K\;{\Delta\sigma}}}$

Wherein Δσ is the difference in σ, K is a constant and u₀ is the focusplane. If σ is very small Δσ equals σ of the out-of-focus plane. Thecluster with the lowest blur value is assigned the depth u₀; all otherclusters are assigned a depth value based on their depth ordering withrespect to the cluster with the lowest radius value. In case we haveonly two clusters, in-focus and out-of-focus, K is positive if theforeground in is focus and negative of the out-of-focus region isforeground.

For single image blur radius estimation, the constants u₀ and K can notbe recovered, for this we would need multiple images with differentfocal settings. However, if we only use the depth map for rendering,most of the time the depth map is translated and scaled anyhow to matchthe capabilities of the screen and the preferences of the user. For anautostereoscopic display device, we may for instance take u₀ in such away that the in-focus region is rendered in the plane of the screen tohave a maximal sharp image. The out-focus region can then be renderedbehind or in front of the screen, depending on the depth ordering.

FIG. 7 shows a method in accordance with the invention. From an input 2Dsignal, image blocks are formed in step 2, block focus characteristics,for instance the block blur radius σ_(B) are determined in step 3, theseblocks are clustered into two or more clusters in step 4. In step 6 therelation between the edge and the region is determined. This may be donedirectly from the focus characteristics, see FIG. 6, or in parallel theimage may be luminance segmented and image edge obtained by luminancesegmentation (step 5) are compared in step 6 to edge determined byclustering wherein comparing the results leads to the determination ofwhich edge belong to which regions and thereby which regions arepositioned in front of which regions, i.e. the depth ordering of regions(step 7). The depth is determined from the focus characteristics (step8) in accordance with a preferred embodiment, which in the examplesgiven is the blur radius, the resulting 3D output signal is provided(step 9).

FIG. 8 shows an image device in accordance with the invention. The imagedevice has means for performing all the steps of the method, i.e. aninput 1, for receiving a 2D input signal, a former 2 for formation imageblocks, a computer 3 for computing block focus characteristics, aclusterer 4 for clustering image regions on basis of focus, an imageedge detector 5, an edge-region relationship determinator 6, a depthorderer 7 and a depth information assigner 8. It furthermore comprisesan output 9 for outputting a 3D signal to a 3D display screen 10. Such adisplay device may for instance an autostereoscopic display device.

FIG. 9 shows a transmitter in accordance with the invention. Thedifference with FIG. 8 is that the display screen itself is not anintegral part of the device. Such a transmitter may for instance readDVD's having a 2D signal and converting the 2D signal into a 3D signalfor use in 3D display device which may be separately sold. It may alsobe a device which makes a DVD having 3D signals from a DVD having a 2Dsignal, the 3D signals may thus be provided to a DVD burner, or forinstance sent to another location. 3D image signals comprisinginformation on the division of the image in regions and the depth orderof the regions and, in preferred embodiments, also the focuscharacteristic of the various regions also form embodiments of theinvention. The information may be given in a header in the signal, whichheader specifies which blocks belongs to the regions, or the dividinglines between the regions, the order of the regions and preferably alsothe focusing characteristics of the regions, preferably the region blurradii. A 3D signal made with the prior art methods and devices does notcomprise such information. A 3D signal in accordance with the inventioncould for instance be generated as follows: A customer has a 3D displaydevice but a normal 2D digital camera. A user sends a 2D home video ordigital image to an internet site. The original 2D signal is convertedinto a 3D signal, which is sent back to the user which can display thevideo or image on his 3D display.

In short the invention can be described as follows:

2D image data are converted into 3D image data. The image is divided, onthe basis of focusing characteristics, into two or more regions, it isdetermined to which region an edge separating two regions belongs. Theregions are depth ordered in accordance with the rule that the rule thata region comprising an edge is closer to the viewer than an adjacentregion and to the regions 3-D depth information is assigned inaccordance with the established depth order of the regions. Preferablyto each of the regions a depth is assigned in dependence on an averageor median focusing characteristic of the region.

The invention is also embodied in any computer program product for amethod or device in accordance with the invention. Under computerprogram product should be understood any physical realization of acollection of commands enabling a processor—generic or special purpose-,after a series of loading steps (which may include intermediateconversion steps, like translation to an intermediate language, and afinal processor language) to get the commands into the processor, toexecute any of the characteristic functions of an invention. Inparticular, the computer program product may be realized as data on acarrier such as e.g. a disk or tape, data present in a memory, datatraveling over a network connection—wired or wireless—, or program codeon paper. Apart from program code, characteristic data required for theprogram may also be embodied as a computer program product.

Some of the steps required for the working of the method may be alreadypresent in the functionality of the processor instead of described inthe computer program product, such as data input and output steps.

It should be noted that the above-mentioned embodiments illustraterather than limit the invention, and that those skilled in the art willbe able to design many alternative embodiments without departing fromthe scope of the appended claims.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim.

The word “comprising” does not exclude the presence of other elements orsteps than those listed in a claim. The invention can be implemented bymeans of hardware comprising several distinct elements, and by means ofa suitably programmed computer. In a device claim enumerating severalmeans, several of these means can be embodied by one and the same itemof hardware. The invention may be implemented by any combination offeatures of various different preferred embodiments as described above.In particular it is mentioned that any embodiment shown or claimed inrelation to an encoding method or encoder has, unless otherwiseindicated or impossible, a corresponding embodiment for a decodingmethod or decoder and such decoding methods and decoder are embodimentsof the invention and claimed herewith.

1. A method of converting image display data wherein 2D image data areconverted into 3D image data comprising the 2D image data and depthinformation, the method comprising: extracting focus information (σ)from the 2D image data, generating at least some of the 3D image datausing the extracted focus information, dividing the 2D image into two ormore regions (C₁, C₂) based on the extracted focus information such thatpixels or blocks of the 2D image are clustered into regions, the regionshaving a respective focusing characteristic (σ_(l), σ₂), the methodcharacterized in that it further comprises: determining a luminance edgenear a transition between two regions of the two or more regions;establishing a depth order between the two regions following the rulethat a region comprising the luminance edge is closer to the viewer thanan adjacent region and assigning depth information to the two regions inaccordance with the established depth order of the two regions.
 2. Themethod as claimed in claim 1, wherein, prior to dividing the image intoregions, a focusing characteristic is assigned on a block basis (3) andthe blocks are clustered into regions (4).
 3. The method as claimed inclaim 1, wherein the 3D depth information is assigned (8) in dependenceon the focusing characteristics (σ₁, σ₂) of the regions (C₁, C₂).
 4. Themethod as claimed in claim 1, wherein the image is divided in tworegions (C₁, C₂).
 5. The method as claimed in claim 1, wherein the imageis divided in three regions (C₁, C₂, C₃).
 6. An apparatus for generating3D image data from a 2D image data input, the apparatus comprising: aninput (1) for 2D image data, an output for outputting at least some ofthe 3D image data, and a converter to convert the input 2D image datainto 3D image data, the converter comprising: a processor to extractfocus information from the 2D image data, cluster the image on the basisof the extracted focus information into two or more regions (C₁, C₂)having a respective focusing characteristic (σ₁, σ₂), determine to whichregion of the image an edge separating two regions belongs, to depthorder the regions following the rule that a region comprising an edge iscloser to the viewer than an adjacent region and assign depthinformation.
 7. A computer program product comprising a plurality ofprogram code portions, stored in a non-transitory computer readablemedium to be loaded by a computer arrangement, comprising instructionsto generate 3D image data on basis of a 2D image data input, thecomputer arrangement comprising processing means wherein theinstructions are arranged for performing a method as claimed in claim 1.8. The method of claim 1, wherein the focus characteristic is a measureof blurriness.
 9. The method of claim 1, wherein the measure ofblurriness is a blur radius.
 10. The method of claim 1, furthercomprising the step of: displaying the 3D image data on a stereoscopicimage display.
 11. The apparatus of claim 6 further comprising a 3Ddisplay screen and wherein the output is arranged for outputting the 3Dimage data to the 3D display screen.
 12. The apparatus of claim 11wherein the 3D display screen is an autostereoscopic display device.